python - ValueError: Shapes (69, 44, 2) and (69, 2, 2) are incompatible -

using tensorflow version 1.3.0 in python 3.5.2. i'm trying build convolution neural network takes in data 69 rows , 44 independent values perform binary classification (that is, either jump or fall), keep getting error:

    valueerror: shapes (69, 44, 2) , (69, 2, 2) incompatible 

i'm not sure code changes size 44 columns down 2. code network i've constructed tensorflow tutorial mnist database has been modified address data/problem. main differences being, i'm using conv1d() instead of conv2d(), , i'm not using pooling layers. code follows:

def cnn_model_fn(features, labels, mode):   """model function cnn."""   # input layer   #-1 filled 69 later on   #44 used independent vars, 1 must used input in tf.layers.conv1d()   input_layer = tf.reshape(features["x"], [-1, 44, 1])    # convolutional layer #1   conv1 = tf.layers.conv1d(       inputs=input_layer,       filters=32,       kernel_size=1,       padding="same",       activation=tf.nn.relu,       name = "alan")    # convolutional layer #2   conv2 = tf.layers.conv1d(       inputs=conv1,       filters=64,       kernel_size=1,       padding="same",       activation=tf.nn.relu,       name = "borris")    # dense layer   dense = tf.layers.dense(inputs=conv2, units=1024, activation=tf.nn.relu)   dropout = tf.layers.dropout(       inputs=dense, rate=0.4, training=mode == tf.estimator.modekeys.train)    # logits layer   logits = tf.layers.dense(inputs=dropout, units=2)   print(logits)    predictions = {       # generate predictions (for predict , eval mode)       "classes": tf.argmax(input=logits, axis=1),       # add `softmax_tensor` graph. used predict ,       # `logging_hook`.       "probabilities": tf.nn.softmax(logits, name="softmax_tensor")   }    if mode == tf.estimator.modekeys.predict:     return tf.estimator.estimatorspec(mode=mode, predictions=predictions)    # calculate loss (for both train , eval modes)   onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=2)   loss = tf.losses.softmax_cross_entropy(       onehot_labels=onehot_labels, logits=logits)    # configure training op (for train mode)   if mode == tf.estimator.modekeys.train:     optimizer = tf.train.adamoptimizer(learning_rate=0.001)     train_op = optimizer.minimize(         loss=loss,         global_step=tf.train.get_global_step())     return tf.estimator.estimatorspec(mode=mode, loss=loss, train_op=train_op)    # add evaluation metrics (for eval mode)   eval_metric_ops = {       "accuracy": tf.metrics.accuracy(           labels=labels, predictions=predictions["classes"])}   return tf.estimator.estimatorspec(       mode=mode, loss=loss, eval_metric_ops=eval_metric_ops) 

here problem occurs, run eval_input_fn:

jumpupdown_classifier = tf.estimator.estimator(     model_fn=cnn_model_fn, model_dir="/tmp/jumpupdown_convnet_model")  tensors_to_log = {"probabilities": "softmax_tensor"} logging_hook = tf.train.loggingtensorhook(tensors=tensors_to_log, every_n_iter=50)  # train model train_input_fn = tf.estimator.inputs.numpy_input_fn(       x={"x": mtraindat},       y=mtrainlab,       batch_size=69,       num_epochs=none,       shuffle=true)  jumpupdown_classifier.train(       input_fn=train_input_fn,       steps=20000,       hooks=[logging_hook])  # evaluate model , print results eval_input_fn = tf.estimator.inputs.numpy_input_fn(       x={"x": mtestdat},       y=mtestlab,       num_epochs=1,       shuffle=false) eval_results = jumpupdown_classifier.evaluate(input_fn=eval_input_fn) print(eval_results) 

to clarify bit more, mtraindat looks following:

array([[ 0.23971076,  0.24687247, -0.22665831, ...,  0.73542702,         -0.57439029, -0.22714323],        [ 0.32290912,  0.61977148, -0.4258523 , ...,  0.73542702,          0.74438596, -0.57915074],        [ 0.41255337,  0.2795991 , -0.56257713, ..., -0.30046052,          0.74438596,  0.74211949],        ...,         [ 0.56027043,  1.63016009,  1.36453617, ...,  0.57338732,          0.74438596,  0.56973094],        [-0.1470048 ,  1.23301661,  0.63612264, ...,  0.73542702,          0.58223492,  0.74211949],        [-0.46544313,  1.02278185,  0.30256116, ..., -1.38631892,          0.74438596,  0.57966185]], dtype=float32) 

where mtrainlab looks this:

array([[ 1.,  0.],    [ 1.,  0.],    [ 0.,  1.],    [ 1.,  0.],    [ 1.,  0.],     ........    [ 0.,  1.],    [ 0.,  1.],    [ 1.,  0.]], dtype=float32) 

and mtestdat , mtestlab similar, contain 6 rows.

i've tried changing values around in attempt diagnose problem, have been unsuccessful. appreciated.